Textannotation

Text annotation is the process of labeling segments of natural language text with metadata to support analysis, retrieval, and modeling. Annotations can describe linguistic structure, semantic roles, entities, sentiment, or other information, and may be applied at the level of characters, words, or larger spans. The goal is to create consistent, machine-readable data for research and applications.

Common annotation types include named-entity recognition (NER) tagging persons, organizations, locations; part-of-speech tagging; syntactic and dependency parsing; semantic role labeling; coreference resolution; sentiment analysis; event and relation annotation; and discourse or rhetorical structure. Annotators follow explicit guidelines to ensure consistency, and datasets often include multiple annotators to measure agreement.

Annotation can be performed manually by trained annotators, by crowdsourcing, or with semi-automatic methods in which models propose labels that humans approve or correct. Data may be stored as inline annotations within the text or as stand-off annotations that reference spans. Formats and schemas vary, with widely used conventions such as CoNLL-style tabular data, TEI, BRAT standoff format, or JSON-based representations.

Applications include creating training data for natural language processing models, linguistic research, information extraction, and improved search and analytics. Challenges include subjectivity, linguistic ambiguity, and cross-annotator agreement. Effective annotation projects rely on clear guidelines, pilot studies, quality control, and calibration sessions.

Common tools for text annotation include BRAT, WebAnno, INCEpTION, Prodigy, and related platforms.